class_docstr: The TextEnvironment enables interaction of a LLM with an environment using tools.
docstr: Compute the reward for a list of histories.
docstr_tok: ["Compute","the","reward","for","a","list","of","histories","."]
code: def compute_reward(self, histories, **reward_kwargs):
      """
      Compute the reward for a list of histories.
      """
      rewards = self.reward_fn(
          [history.last_text_segment for history in histories],
          **reward_kwargs,
      )
      for history, reward in zip(histories, rewards):
          history.reward = reward
      return histories
  
imports: import re
  import warnings
  import torch
  from accelerate.utils import extract_model_from_parallel
  from transformers import StoppingCriteria, StoppingCriteriaList
  from ..import_utils import is_rich_available
label_desc: Use image node_id 5 for calling the TextEnvironment obj's underlying member method code with example usage: obj.compute_reward(histories) and returns: histories